Multi-Trajectory Models of Chronic Kidney Disease Progression

Area of Science:

• Nephrology
• Data Science
• Public Health

Background:

• Chronic kidney disease (CKD) is a growing global health concern.
• Understanding CKD progression and complications is crucial for healthcare systems.
• Existing data lacks comprehensive models for CKD patient stratification.

Purpose of the Study:

• To identify distinct patient risk groups in chronic kidney disease.
• To uncover typical progressions of CKD and associated comorbidities.
• To develop accurate methods for classifying new patients into identified risk groups.

Main Methods:

• Utilized a large dataset of Electronic Health Records (EHRs) from over 33,000 patients.
• Applied group-based trajectory modeling (GBTM) to analyze patient data.
• Developed a classification system for new patients based on identified trajectories.

Main Results:

• Identified distinct patient risk groups with unique CKD progression trajectories.
• Achieved high accuracy (up to 90%) in classifying new patients into these groups.
• Demonstrated the utility of multitrajectory modeling for understanding CKD development.

Conclusions:

• Group-based trajectory modeling (GBTM) effectively reveals diverse CKD patient pathways.
• This approach enhances understanding of CKD progression and complication interactions.
• Accurate patient stratification can potentially improve CKD management strategies.